Decisioning Engine for Auto-Coding Reporting Parameters
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Solution Overview
Problem
Conventional systems are unable to process and transform incoming resources with varying file formats and naming conventions to auto-code reporting parameters, which are necessary for generating accurate bills, particularly in emergency medical services but applicable to other service-based billing scenarios.
Innovation Solution
A system comprising a decisioning engine that processes incoming resources, applies top and post rules to extract and auto-code reporting parameters like Level of Service, Medical Necessity, and priority, using a rules database to determine decisions based on input parameters and predefined conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional systems process incoming resources with varying file formats and naming conventions, then the system can handle diverse data sources, but the system cannot auto-code reporting parameters accurately
Solution Approach 1:
The patent introduces an intermediary processing layer that includes a file format detection module, data extraction module, and parameter mapping module. This intermediary layer translates various incoming file formats into a standardized internal representation, enabling accurate auto-coding of reporting parameters regardless of the source format. The intermediary acts as a bridge between diverse data sources and the billing system's requirements.
Solution Approach 2:
The system dynamically changes processing parameters based on the detected file format. When different file formats are identified, the system adjusts extraction rules, field mappings, and transformation logic accordingly. This parameter adaptation allows the system to maintain high accuracy in auto-coding reporting parameters across multiple file formats and naming conventions.
2Reliability
If manual processing is used to ensure accurate billing, then billing accuracy is maintained, but processing time and operational costs increase
Solution Approach 1:
The system implements self-service through automated decisioning engines that independently analyze incoming resources, extract relevant parameters, and generate billing information without human intervention. The auto-coding capability allows the system to make intelligent decisions about reporting parameters based on predefined rules and algorithms, maintaining billing accuracy while eliminating manual processing steps and reducing time loss.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously learns from processed data and refines its auto-coding accuracy. By implementing validation rules and cross-checking mechanisms, the system provides feedback loops that ensure billing accuracy is maintained while operating in automated mode, thereby reducing processing time without sacrificing reliability.
3Productivity
If automated processing is implemented to reduce manual work, then processing efficiency improves, but the system cannot handle varying file formats and naming conventions
Solution Approach 1:
The patent designs a universal processing framework that can handle multiple file formats, naming conventions, and data sources through a single automated system. The system incorporates format-agnostic data extraction capabilities and configurable mapping rules that enable it to process diverse incoming resources efficiently. This multi-functional approach maintains high processing efficiency while simultaneously adapting to various data source requirements.
Solution Approach 2:
The system employs dynamic configuration capabilities where processing rules and parameters can be adjusted based on the incoming data format. The automated processing pipeline is designed to be flexible and adaptive, dynamically selecting appropriate extraction and transformation rules based on file format detection, thereby maintaining both high productivity and versatility across different data sources.
4Measurement precision
If complex processing rules are applied to ensure accurate parameter coding, then coding accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the complex processing rules into modular, manageable components organized by parameter type and file format. Each module handles specific aspects of the auto-coding process independently, making the overall complex system easier to maintain and update. This segmentation allows high coding accuracy through comprehensive rule sets while managing system complexity through modular architecture.
Solution Approach 2:
The system applies processing rules selectively based on the detected file format and required reporting parameters. Rather than applying all possible rules to every input, the system uses conditional logic to activate only the necessary subset of rules for each specific case. This partial action approach maintains coding accuracy for required parameters while reducing unnecessary system complexity and processing overhead.
Data Source
AI summary
Embodiments of the present invention provide a system for processing and transforming incoming resources to auto-code reporting parameters. In particular, the system may be configured to receive one or more resources from one or more data sources, pre-process the one or more resources to extract one or more input parameters, process the one or more input parameters, via a decisioning engine, auto-code one or more reporting parameters based on processing the one or more input parameters, via the decisioning engine, check for predetermined conditions based on one or more predetermined rules, and prepare an output file comprising the auto-coded one or more reporting parameters and the predetermined conditions.